Advanced search options

Sorted by: relevance · author · university · date | New search

You searched for `subject:(Tile Low Rank Approximations)`

.
Showing records 1 – 30 of
16055 total matches.

◁ [1] [2] [3] [4] [5] … [536] ▶

Search Limiters

Dates

- 2017 – 2021 (4861)
- 2012 – 2016 (6291)
- 2007 – 2011 (3516)
- 2002 – 2006 (1162)
- 1997 – 2001 (388)
- 1992 – 1996 (222)
- 1987 – 1991 (143)
- 1982 – 1986 (85)
- 1977 – 1981 (62)
- 1972 – 1976 (59)

Universities

- Brno University of Technology (766)
- University of São Paulo (474)
- University of Michigan (308)
- Delft University of Technology (297)
- NSYSU (251)
- University of Florida (251)
- Virginia Tech (250)
- Brazil (243)
- Texas A&M University (225)
- Georgia Tech (223)
- University of Texas – Austin (222)
- University of Illinois – Urbana-Champaign (193)
- National University of Singapore (172)
- University of Waterloo (158)
- Penn State University (145)

Department

- Electrical Engineering (429)
- Electrical and Computer Engineering (360)
- Mechanical Engineering (224)
- Physics (206)
- Civil Engineering (108)
- Computer Science (81)
- Psychology (79)
- Mathematics (76)
- Aerospace Engineering (72)
- Chemistry (71)
- Materials Science and Engineering (71)
- Chemical Engineering (70)
- Computer Science and Engineering (64)
- Petroleum Engineering (56)
- Physique (50)

Degrees

Levels

- doctoral (5841)
- masters (3621)
- thesis (305)
- dissertation (22)
- doctor of philosophy (ph.d.) (12)
- project/capstone (11)

Languages

Country

- US (6051)
- Brazil (1789)
- Canada (997)
- France (917)
- Czech Republic (773)
- Sweden (753)
- Netherlands (619)
- UK (616)
- Australia (500)
- South Africa (427)
- Greece (307)
- Taiwan (251)
- Portugal (227)
- New Zealand (199)
- Japan (196)

▼ Search Limiters

King Abdullah University of Science and Technology

1. Alharthi, Noha. Fast High-order Integral Equation Solvers for Acoustic and Electromagnetic Scattering Problems.

Degree: Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, 2019, King Abdullah University of Science and Technology

URL: http://hdl.handle.net/10754/660105

► Acoustic and electromagnetic scattering from arbitrarily shaped structures can be numerically characterized by solving various surface integral equations (SIEs). One of the most effective techniques…
(more)

Subjects/Keywords: Boundary Integral Equation; Acoustic Scattering; LU-Based Solver; Fast Solvers; Fast Multipole Solvers; Tile Low-Rank Approximations

Record Details Similar Records

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Alharthi, N. (2019). Fast High-order Integral Equation Solvers for Acoustic and Electromagnetic Scattering Problems. (Thesis). King Abdullah University of Science and Technology. Retrieved from http://hdl.handle.net/10754/660105

Note: this citation may be lacking information needed for this citation format:

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Alharthi, Noha. “Fast High-order Integral Equation Solvers for Acoustic and Electromagnetic Scattering Problems.” 2019. Thesis, King Abdullah University of Science and Technology. Accessed April 18, 2021. http://hdl.handle.net/10754/660105.

Note: this citation may be lacking information needed for this citation format:

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Alharthi, Noha. “Fast High-order Integral Equation Solvers for Acoustic and Electromagnetic Scattering Problems.” 2019. Web. 18 Apr 2021.

Vancouver:

Alharthi N. Fast High-order Integral Equation Solvers for Acoustic and Electromagnetic Scattering Problems. [Internet] [Thesis]. King Abdullah University of Science and Technology; 2019. [cited 2021 Apr 18]. Available from: http://hdl.handle.net/10754/660105.

Note: this citation may be lacking information needed for this citation format:

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Alharthi N. Fast High-order Integral Equation Solvers for Acoustic and Electromagnetic Scattering Problems. [Thesis]. King Abdullah University of Science and Technology; 2019. Available from: http://hdl.handle.net/10754/660105

Not specified: Masters Thesis or Doctoral Dissertation

University of Colorado

2.
Fairbanks, Hillary Ruth.
* Low*-

Degree: PhD, 2018, University of Colorado

URL: https://scholar.colorado.edu/appm_gradetds/114

► Characterizing and incorporating uncertainties when simulating physical phenomena is essential for improving model-based predictions. These uncertainties may stem from a lack of knowledge regarding…
(more)

Subjects/Keywords: bi-fidelity approximations; low-rank approximations; multi-fidelity approximations; parametric model reduction; uncertainty quantification; Applied Mathematics; Models and Methods

Record Details Similar Records

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Fairbanks, H. R. (2018). Low-Rank, Multi-Fidelity Methods for Uncertainty Quantification of High-Dimensional Systems. (Doctoral Dissertation). University of Colorado. Retrieved from https://scholar.colorado.edu/appm_gradetds/114

Chicago Manual of Style (16^{th} Edition):

Fairbanks, Hillary Ruth. “Low-Rank, Multi-Fidelity Methods for Uncertainty Quantification of High-Dimensional Systems.” 2018. Doctoral Dissertation, University of Colorado. Accessed April 18, 2021. https://scholar.colorado.edu/appm_gradetds/114.

MLA Handbook (7^{th} Edition):

Fairbanks, Hillary Ruth. “Low-Rank, Multi-Fidelity Methods for Uncertainty Quantification of High-Dimensional Systems.” 2018. Web. 18 Apr 2021.

Vancouver:

Fairbanks HR. Low-Rank, Multi-Fidelity Methods for Uncertainty Quantification of High-Dimensional Systems. [Internet] [Doctoral dissertation]. University of Colorado; 2018. [cited 2021 Apr 18]. Available from: https://scholar.colorado.edu/appm_gradetds/114.

Council of Science Editors:

Fairbanks HR. Low-Rank, Multi-Fidelity Methods for Uncertainty Quantification of High-Dimensional Systems. [Doctoral Dissertation]. University of Colorado; 2018. Available from: https://scholar.colorado.edu/appm_gradetds/114

Universidade Nova

3. Rodrigues, Paulo Jorge Canas. New strategies to detect and understand genotype-by-environment interactions and QTL-by-environment interactions.

Degree: 2012, Universidade Nova

URL: http://www.rcaap.pt/detail.jsp?id=oai:run.unl.pt:10362/7848

►

Dissertação para obtenção do Grau de Doutor em Estatística e Gestão do Risco, especialidade em Estatística

Genotype-by-environment interaction (GEI) is frequent in multi-environment trials, and… (more)

Subjects/Keywords: Genotype-by-environment interaction; QTL-by-environment interaction; AMMI models; Low-rank approximations; Weighted low-rank approximations; Eco-physiological crop growth models

Record Details Similar Records

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Rodrigues, P. J. C. (2012). New strategies to detect and understand genotype-by-environment interactions and QTL-by-environment interactions. (Thesis). Universidade Nova. Retrieved from http://www.rcaap.pt/detail.jsp?id=oai:run.unl.pt:10362/7848

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Rodrigues, Paulo Jorge Canas. “New strategies to detect and understand genotype-by-environment interactions and QTL-by-environment interactions.” 2012. Thesis, Universidade Nova. Accessed April 18, 2021. http://www.rcaap.pt/detail.jsp?id=oai:run.unl.pt:10362/7848.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Rodrigues, Paulo Jorge Canas. “New strategies to detect and understand genotype-by-environment interactions and QTL-by-environment interactions.” 2012. Web. 18 Apr 2021.

Vancouver:

Rodrigues PJC. New strategies to detect and understand genotype-by-environment interactions and QTL-by-environment interactions. [Internet] [Thesis]. Universidade Nova; 2012. [cited 2021 Apr 18]. Available from: http://www.rcaap.pt/detail.jsp?id=oai:run.unl.pt:10362/7848.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Rodrigues PJC. New strategies to detect and understand genotype-by-environment interactions and QTL-by-environment interactions. [Thesis]. Universidade Nova; 2012. Available from: http://www.rcaap.pt/detail.jsp?id=oai:run.unl.pt:10362/7848

Not specified: Masters Thesis or Doctoral Dissertation

King Abdullah University of Science and Technology

4. Charara, Ali. Exploiting Data Sparsity In Covariance Matrix Computations on Heterogeneous Systems.

Degree: Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, 2018, King Abdullah University of Science and Technology

URL: http://hdl.handle.net/10754/627948

► Covariance matrices are ubiquitous in computational sciences, typically describing the correlation of elements of large multivariate spatial data sets. For example, covari- ance matrices are…
(more)

Subjects/Keywords: data sparse; Hierarchical; covariance matrix; GPU; tile low-rank; Dense Linear Algebra

Record Details Similar Records

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Charara, A. (2018). Exploiting Data Sparsity In Covariance Matrix Computations on Heterogeneous Systems. (Thesis). King Abdullah University of Science and Technology. Retrieved from http://hdl.handle.net/10754/627948

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Charara, Ali. “Exploiting Data Sparsity In Covariance Matrix Computations on Heterogeneous Systems.” 2018. Thesis, King Abdullah University of Science and Technology. Accessed April 18, 2021. http://hdl.handle.net/10754/627948.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Charara, Ali. “Exploiting Data Sparsity In Covariance Matrix Computations on Heterogeneous Systems.” 2018. Web. 18 Apr 2021.

Vancouver:

Charara A. Exploiting Data Sparsity In Covariance Matrix Computations on Heterogeneous Systems. [Internet] [Thesis]. King Abdullah University of Science and Technology; 2018. [cited 2021 Apr 18]. Available from: http://hdl.handle.net/10754/627948.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Charara A. Exploiting Data Sparsity In Covariance Matrix Computations on Heterogeneous Systems. [Thesis]. King Abdullah University of Science and Technology; 2018. Available from: http://hdl.handle.net/10754/627948

Not specified: Masters Thesis or Doctoral Dissertation

5. Ayala Obregón, Alan. Complexity reduction methods applied to the rapid solution to multi-trace boundary integral formulations : Méthodes de réduction de complexité appliquées à la résolution rapide des formulations intégrales de bord de type multi-trace.

Degree: Docteur es, Mathématiques appliquées, 2018, Sorbonne université

URL: http://www.theses.fr/2018SORUS581

►

L'objectif de cette thèse est de fournir des techniques de réduction de complexité pour la solution des équations intégrales de frontière (BIE). En particulier, nous… (more)

Subjects/Keywords: Formulation multi-trace; Équation de Maxwell; Rang faible; Sous-espaces affines; Algorithme de communication optimale; Approximation CUR; Multi-trace formulation; Maxwell equations; Low-rank approximations

Record Details Similar Records

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Ayala Obregón, A. (2018). Complexity reduction methods applied to the rapid solution to multi-trace boundary integral formulations : Méthodes de réduction de complexité appliquées à la résolution rapide des formulations intégrales de bord de type multi-trace. (Doctoral Dissertation). Sorbonne université. Retrieved from http://www.theses.fr/2018SORUS581

Chicago Manual of Style (16^{th} Edition):

Ayala Obregón, Alan. “Complexity reduction methods applied to the rapid solution to multi-trace boundary integral formulations : Méthodes de réduction de complexité appliquées à la résolution rapide des formulations intégrales de bord de type multi-trace.” 2018. Doctoral Dissertation, Sorbonne université. Accessed April 18, 2021. http://www.theses.fr/2018SORUS581.

MLA Handbook (7^{th} Edition):

Ayala Obregón, Alan. “Complexity reduction methods applied to the rapid solution to multi-trace boundary integral formulations : Méthodes de réduction de complexité appliquées à la résolution rapide des formulations intégrales de bord de type multi-trace.” 2018. Web. 18 Apr 2021.

Vancouver:

Ayala Obregón A. Complexity reduction methods applied to the rapid solution to multi-trace boundary integral formulations : Méthodes de réduction de complexité appliquées à la résolution rapide des formulations intégrales de bord de type multi-trace. [Internet] [Doctoral dissertation]. Sorbonne université; 2018. [cited 2021 Apr 18]. Available from: http://www.theses.fr/2018SORUS581.

Council of Science Editors:

Ayala Obregón A. Complexity reduction methods applied to the rapid solution to multi-trace boundary integral formulations : Méthodes de réduction de complexité appliquées à la résolution rapide des formulations intégrales de bord de type multi-trace. [Doctoral Dissertation]. Sorbonne université; 2018. Available from: http://www.theses.fr/2018SORUS581

INP Toulouse

6.
Weisbecker, Clément.
Improving multifrontal solvers by means of algebraic Block *Low*-*Rank* representations : Amélioration des solveurs multifrontaux à l’aide de representations algébriques rang-faible par blocs.

Degree: Docteur es, Sûreté du logiciel et calcul haute performance, 2013, INP Toulouse

URL: http://www.theses.fr/2013INPT0134

► Nous considérons la résolution de très grands systèmes linéaires creux à l'aide d'une méthode de factorisation directe appelée méthode multifrontale. Bien que numériquement robustes et…
(more)

Subjects/Keywords: Matrices creuses; Systèmes linéaires creux; Méthodes directes; Méthode multifrontale; Approximations rang-faible; Equations aux dérivées partielles elliptiques; Sparse matrices; Direct methods for linear systems; Multifrontal method; Low-rank approximations; High-performance computing; Parallel computing

Record Details Similar Records

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Weisbecker, C. (2013). Improving multifrontal solvers by means of algebraic Block Low-Rank representations : Amélioration des solveurs multifrontaux à l’aide de representations algébriques rang-faible par blocs. (Doctoral Dissertation). INP Toulouse. Retrieved from http://www.theses.fr/2013INPT0134

Chicago Manual of Style (16^{th} Edition):

Weisbecker, Clément. “Improving multifrontal solvers by means of algebraic Block Low-Rank representations : Amélioration des solveurs multifrontaux à l’aide de representations algébriques rang-faible par blocs.” 2013. Doctoral Dissertation, INP Toulouse. Accessed April 18, 2021. http://www.theses.fr/2013INPT0134.

MLA Handbook (7^{th} Edition):

Weisbecker, Clément. “Improving multifrontal solvers by means of algebraic Block Low-Rank representations : Amélioration des solveurs multifrontaux à l’aide de representations algébriques rang-faible par blocs.” 2013. Web. 18 Apr 2021.

Vancouver:

Weisbecker C. Improving multifrontal solvers by means of algebraic Block Low-Rank representations : Amélioration des solveurs multifrontaux à l’aide de representations algébriques rang-faible par blocs. [Internet] [Doctoral dissertation]. INP Toulouse; 2013. [cited 2021 Apr 18]. Available from: http://www.theses.fr/2013INPT0134.

Council of Science Editors:

Weisbecker C. Improving multifrontal solvers by means of algebraic Block Low-Rank representations : Amélioration des solveurs multifrontaux à l’aide de representations algébriques rang-faible par blocs. [Doctoral Dissertation]. INP Toulouse; 2013. Available from: http://www.theses.fr/2013INPT0134

7.
Mary, Théo.
Solveurs multifrontaux exploitant des blocs de rang faible : complexité, performance et parallélisme : Block *low*-*rank* multifrontal solvers : complexity, performance, and scalability.

Degree: Docteur es, Mathématiques, 2017, Université Toulouse III – Paul Sabatier

URL: http://www.theses.fr/2017TOU30305

►

Nous nous intéressons à l'utilisation d'*approximations* de rang faible pour réduire le coût des solveurs creux directs multifrontaux. Parmi les différents formats matriciels qui ont…
(more)

Subjects/Keywords: Matrices creuses; Systèmes linéaires creux; Méthodes directes; Méthode multifrontale; Approximations de rang-faible; Equations aux dérivées partielles elliptiques; Calcul haute performance; Calcul parallèle; Sparse matrices; Direct methods for linear systems; Multifrontal method; Low-rank approximations; High-performance computing; Parallel computing; Partial differential equations

Record Details Similar Records

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Mary, T. (2017). Solveurs multifrontaux exploitant des blocs de rang faible : complexité, performance et parallélisme : Block low-rank multifrontal solvers : complexity, performance, and scalability. (Doctoral Dissertation). Université Toulouse III – Paul Sabatier. Retrieved from http://www.theses.fr/2017TOU30305

Chicago Manual of Style (16^{th} Edition):

Mary, Théo. “Solveurs multifrontaux exploitant des blocs de rang faible : complexité, performance et parallélisme : Block low-rank multifrontal solvers : complexity, performance, and scalability.” 2017. Doctoral Dissertation, Université Toulouse III – Paul Sabatier. Accessed April 18, 2021. http://www.theses.fr/2017TOU30305.

MLA Handbook (7^{th} Edition):

Mary, Théo. “Solveurs multifrontaux exploitant des blocs de rang faible : complexité, performance et parallélisme : Block low-rank multifrontal solvers : complexity, performance, and scalability.” 2017. Web. 18 Apr 2021.

Vancouver:

Mary T. Solveurs multifrontaux exploitant des blocs de rang faible : complexité, performance et parallélisme : Block low-rank multifrontal solvers : complexity, performance, and scalability. [Internet] [Doctoral dissertation]. Université Toulouse III – Paul Sabatier; 2017. [cited 2021 Apr 18]. Available from: http://www.theses.fr/2017TOU30305.

Council of Science Editors:

Mary T. Solveurs multifrontaux exploitant des blocs de rang faible : complexité, performance et parallélisme : Block low-rank multifrontal solvers : complexity, performance, and scalability. [Doctoral Dissertation]. Université Toulouse III – Paul Sabatier; 2017. Available from: http://www.theses.fr/2017TOU30305

Georgia Tech

8.
Hayes, Charles Ethan.
* Low*-

Degree: PhD, Electrical and Computer Engineering, 2020, Georgia Tech

URL: http://hdl.handle.net/1853/63587

► The objective of this research is to improve the signal processing of electromagnetic induction (EMI) sensors for detection, localization, characterization, and classification of targets buried…
(more)

Subjects/Keywords: EMI; Electromagnetic induction; Low rank

Record Details Similar Records

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Hayes, C. E. (2020). Low-rank model exploitation of electromagnetic induction sensors. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/63587

Chicago Manual of Style (16^{th} Edition):

Hayes, Charles Ethan. “Low-rank model exploitation of electromagnetic induction sensors.” 2020. Doctoral Dissertation, Georgia Tech. Accessed April 18, 2021. http://hdl.handle.net/1853/63587.

MLA Handbook (7^{th} Edition):

Hayes, Charles Ethan. “Low-rank model exploitation of electromagnetic induction sensors.” 2020. Web. 18 Apr 2021.

Vancouver:

Hayes CE. Low-rank model exploitation of electromagnetic induction sensors. [Internet] [Doctoral dissertation]. Georgia Tech; 2020. [cited 2021 Apr 18]. Available from: http://hdl.handle.net/1853/63587.

Council of Science Editors:

Hayes CE. Low-rank model exploitation of electromagnetic induction sensors. [Doctoral Dissertation]. Georgia Tech; 2020. Available from: http://hdl.handle.net/1853/63587

Georgia Tech

9.
Xing, Xin.
The proxy point method for *rank*-structured matrices.

Degree: PhD, Mathematics, 2019, Georgia Tech

URL: http://hdl.handle.net/1853/62327

► *Rank*-structured matrix representations, e.g., \mathcal{H}^{2} and HSS, are commonly used to reduce computation and storage cost for dense matrices defined by interactions between many bodies.…
(more)

Subjects/Keywords: Rank-structured matrices; Low-rank approximation; Kernel matrices; Numerical linear algebra

Record Details Similar Records

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Xing, X. (2019). The proxy point method for rank-structured matrices. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/62327

Chicago Manual of Style (16^{th} Edition):

Xing, Xin. “The proxy point method for rank-structured matrices.” 2019. Doctoral Dissertation, Georgia Tech. Accessed April 18, 2021. http://hdl.handle.net/1853/62327.

MLA Handbook (7^{th} Edition):

Xing, Xin. “The proxy point method for rank-structured matrices.” 2019. Web. 18 Apr 2021.

Vancouver:

Xing X. The proxy point method for rank-structured matrices. [Internet] [Doctoral dissertation]. Georgia Tech; 2019. [cited 2021 Apr 18]. Available from: http://hdl.handle.net/1853/62327.

Council of Science Editors:

Xing X. The proxy point method for rank-structured matrices. [Doctoral Dissertation]. Georgia Tech; 2019. Available from: http://hdl.handle.net/1853/62327

10. Löffler, Matthias. Statistical inference in high-dimensional matrix models.

Degree: PhD, 2020, University of Cambridge

URL: https://www.repository.cam.ac.uk/handle/1810/298064

► Matrix models are ubiquitous in modern statistics. For instance, they are used in finance to assess interdependence of assets, in genomics to impute missing data…
(more)

Subjects/Keywords: High-dimensional Statistics; Low-rank inference; PCA

Record Details Similar Records

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Löffler, M. (2020). Statistical inference in high-dimensional matrix models. (Doctoral Dissertation). University of Cambridge. Retrieved from https://www.repository.cam.ac.uk/handle/1810/298064

Chicago Manual of Style (16^{th} Edition):

Löffler, Matthias. “Statistical inference in high-dimensional matrix models.” 2020. Doctoral Dissertation, University of Cambridge. Accessed April 18, 2021. https://www.repository.cam.ac.uk/handle/1810/298064.

MLA Handbook (7^{th} Edition):

Löffler, Matthias. “Statistical inference in high-dimensional matrix models.” 2020. Web. 18 Apr 2021.

Vancouver:

Löffler M. Statistical inference in high-dimensional matrix models. [Internet] [Doctoral dissertation]. University of Cambridge; 2020. [cited 2021 Apr 18]. Available from: https://www.repository.cam.ac.uk/handle/1810/298064.

Council of Science Editors:

Löffler M. Statistical inference in high-dimensional matrix models. [Doctoral Dissertation]. University of Cambridge; 2020. Available from: https://www.repository.cam.ac.uk/handle/1810/298064

University of Illinois – Urbana-Champaign

11.
Gui, Huan.
* Low*-

Degree: PhD, Computer Science, 2017, University of Illinois – Urbana-Champaign

URL: http://hdl.handle.net/2142/98280

► In many real-world applications of data mining, datasets can be represented using matrices, where rows of the matrix correspond to objects (or data instances) and…
(more)

Subjects/Keywords: Low-rank model; Embedding learning; Noncovex

Record Details Similar Records

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Gui, H. (2017). Low-rank estimation and embedding learning: theory and applications. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/98280

Chicago Manual of Style (16^{th} Edition):

Gui, Huan. “Low-rank estimation and embedding learning: theory and applications.” 2017. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed April 18, 2021. http://hdl.handle.net/2142/98280.

MLA Handbook (7^{th} Edition):

Gui, Huan. “Low-rank estimation and embedding learning: theory and applications.” 2017. Web. 18 Apr 2021.

Vancouver:

Gui H. Low-rank estimation and embedding learning: theory and applications. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2017. [cited 2021 Apr 18]. Available from: http://hdl.handle.net/2142/98280.

Council of Science Editors:

Gui H. Low-rank estimation and embedding learning: theory and applications. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2017. Available from: http://hdl.handle.net/2142/98280

University of Cambridge

12. Löffler, Matthias. Statistical inference in high-dimensional matrix models.

Degree: PhD, 2020, University of Cambridge

URL: https://doi.org/10.17863/CAM.45122 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.793044

► Matrix models are ubiquitous in modern statistics. For instance, they are used in finance to assess interdependence of assets, in genomics to impute missing data…
(more)

Subjects/Keywords: High-dimensional Statistics; Low-rank inference; PCA

Record Details Similar Records

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Löffler, M. (2020). Statistical inference in high-dimensional matrix models. (Doctoral Dissertation). University of Cambridge. Retrieved from https://doi.org/10.17863/CAM.45122 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.793044

Chicago Manual of Style (16^{th} Edition):

Löffler, Matthias. “Statistical inference in high-dimensional matrix models.” 2020. Doctoral Dissertation, University of Cambridge. Accessed April 18, 2021. https://doi.org/10.17863/CAM.45122 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.793044.

MLA Handbook (7^{th} Edition):

Löffler, Matthias. “Statistical inference in high-dimensional matrix models.” 2020. Web. 18 Apr 2021.

Vancouver:

Löffler M. Statistical inference in high-dimensional matrix models. [Internet] [Doctoral dissertation]. University of Cambridge; 2020. [cited 2021 Apr 18]. Available from: https://doi.org/10.17863/CAM.45122 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.793044.

Council of Science Editors:

Löffler M. Statistical inference in high-dimensional matrix models. [Doctoral Dissertation]. University of Cambridge; 2020. Available from: https://doi.org/10.17863/CAM.45122 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.793044

University of Iowa

13.
Bhattacharya, Ipshita.
Pushing the limits of spectroscopic imaging using novel *low*-*rank* based reconstruction algorithm.

Degree: PhD, Electrical and Computer Engineering, 2017, University of Iowa

URL: https://ir.uiowa.edu/etd/6058

► Non-invasively reosolving spatial distribution of tissue metabolites serves as a diagnostic tool to in-vivo metabolism thus making magnetic resonance spectroscopic imaging (MRSI) a very…
(more)

Subjects/Keywords: Low-rank based algorithms; Magnetic Resonance Imaging; Optimization Algorithm; Spectroscopy; Structured low-rank; Electrical and Computer Engineering

Record Details Similar Records

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Bhattacharya, I. (2017). Pushing the limits of spectroscopic imaging using novel low-rank based reconstruction algorithm. (Doctoral Dissertation). University of Iowa. Retrieved from https://ir.uiowa.edu/etd/6058

Chicago Manual of Style (16^{th} Edition):

Bhattacharya, Ipshita. “Pushing the limits of spectroscopic imaging using novel low-rank based reconstruction algorithm.” 2017. Doctoral Dissertation, University of Iowa. Accessed April 18, 2021. https://ir.uiowa.edu/etd/6058.

MLA Handbook (7^{th} Edition):

Bhattacharya, Ipshita. “Pushing the limits of spectroscopic imaging using novel low-rank based reconstruction algorithm.” 2017. Web. 18 Apr 2021.

Vancouver:

Bhattacharya I. Pushing the limits of spectroscopic imaging using novel low-rank based reconstruction algorithm. [Internet] [Doctoral dissertation]. University of Iowa; 2017. [cited 2021 Apr 18]. Available from: https://ir.uiowa.edu/etd/6058.

Council of Science Editors:

Bhattacharya I. Pushing the limits of spectroscopic imaging using novel low-rank based reconstruction algorithm. [Doctoral Dissertation]. University of Iowa; 2017. Available from: https://ir.uiowa.edu/etd/6058

Rochester Institute of Technology

14.
Song, Ge.
* Low*-

Degree: MS, 2019, Rochester Institute of Technology

URL: https://scholarworks.rit.edu/theses/10310

► The human brain is hard to study and analysis, not because of the complexity of the brain structure, such as neurons and neurons connections,…
(more)

Subjects/Keywords: Brain regions; Brain scans; fMRI; Low-rank multivariate general linear model

Record Details Similar Records

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Song, G. (2019). Low-Rank Multivariate General Linear Model with Relationship between Brain Regions and Time Points. (Masters Thesis). Rochester Institute of Technology. Retrieved from https://scholarworks.rit.edu/theses/10310

Chicago Manual of Style (16^{th} Edition):

Song, Ge. “Low-Rank Multivariate General Linear Model with Relationship between Brain Regions and Time Points.” 2019. Masters Thesis, Rochester Institute of Technology. Accessed April 18, 2021. https://scholarworks.rit.edu/theses/10310.

MLA Handbook (7^{th} Edition):

Song, Ge. “Low-Rank Multivariate General Linear Model with Relationship between Brain Regions and Time Points.” 2019. Web. 18 Apr 2021.

Vancouver:

Song G. Low-Rank Multivariate General Linear Model with Relationship between Brain Regions and Time Points. [Internet] [Masters thesis]. Rochester Institute of Technology; 2019. [cited 2021 Apr 18]. Available from: https://scholarworks.rit.edu/theses/10310.

Council of Science Editors:

Song G. Low-Rank Multivariate General Linear Model with Relationship between Brain Regions and Time Points. [Masters Thesis]. Rochester Institute of Technology; 2019. Available from: https://scholarworks.rit.edu/theses/10310

Temple University

15.
Shank, Stephen David.
* Low*-

Degree: PhD, 2014, Temple University

URL: http://digital.library.temple.edu/u?/p245801coll10,273331

►

Mathematics

We consider *low*-*rank* solution methods for certain classes of large-scale linear matrix equations. Our aim is to adapt existing *low*-*rank* solution methods based on…
(more)

Subjects/Keywords: Applied mathematics;

Record Details Similar Records

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Shank, S. D. (2014). Low-rank solution methods for large-scale linear matrix equations. (Doctoral Dissertation). Temple University. Retrieved from http://digital.library.temple.edu/u?/p245801coll10,273331

Chicago Manual of Style (16^{th} Edition):

Shank, Stephen David. “Low-rank solution methods for large-scale linear matrix equations.” 2014. Doctoral Dissertation, Temple University. Accessed April 18, 2021. http://digital.library.temple.edu/u?/p245801coll10,273331.

MLA Handbook (7^{th} Edition):

Shank, Stephen David. “Low-rank solution methods for large-scale linear matrix equations.” 2014. Web. 18 Apr 2021.

Vancouver:

Shank SD. Low-rank solution methods for large-scale linear matrix equations. [Internet] [Doctoral dissertation]. Temple University; 2014. [cited 2021 Apr 18]. Available from: http://digital.library.temple.edu/u?/p245801coll10,273331.

Council of Science Editors:

Shank SD. Low-rank solution methods for large-scale linear matrix equations. [Doctoral Dissertation]. Temple University; 2014. Available from: http://digital.library.temple.edu/u?/p245801coll10,273331

University of Alberta

16.
Li,Qiang.
Hydrothermal Treatment of *Low* *Rank* Coal for Making High
Solid Loading and Stable Coal Water Slurries.

Degree: MS, Department of Chemical and Materials Engineering, 2014, University of Alberta

URL: https://era.library.ualberta.ca/files/xs55mc577

► The objective of this research is to understand the effect of hydrothermal dewatering (HTD) on surface properties, stability and rheological behavior of lignite water slurry…
(more)

Subjects/Keywords: Lignite; coal water slurry; Rheology; Low rank coal; Hydrothermal treatment

Record Details Similar Records

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Li,Qiang. (2014). Hydrothermal Treatment of Low Rank Coal for Making High Solid Loading and Stable Coal Water Slurries. (Masters Thesis). University of Alberta. Retrieved from https://era.library.ualberta.ca/files/xs55mc577

Note: this citation may be lacking information needed for this citation format:

Author name may be incomplete

Chicago Manual of Style (16^{th} Edition):

Li,Qiang. “Hydrothermal Treatment of Low Rank Coal for Making High Solid Loading and Stable Coal Water Slurries.” 2014. Masters Thesis, University of Alberta. Accessed April 18, 2021. https://era.library.ualberta.ca/files/xs55mc577.

Note: this citation may be lacking information needed for this citation format:

Author name may be incomplete

MLA Handbook (7^{th} Edition):

Li,Qiang. “Hydrothermal Treatment of Low Rank Coal for Making High Solid Loading and Stable Coal Water Slurries.” 2014. Web. 18 Apr 2021.

Note: this citation may be lacking information needed for this citation format:

Author name may be incomplete

Vancouver:

Li,Qiang. Hydrothermal Treatment of Low Rank Coal for Making High Solid Loading and Stable Coal Water Slurries. [Internet] [Masters thesis]. University of Alberta; 2014. [cited 2021 Apr 18]. Available from: https://era.library.ualberta.ca/files/xs55mc577.

Author name may be incomplete

Council of Science Editors:

Li,Qiang. Hydrothermal Treatment of Low Rank Coal for Making High Solid Loading and Stable Coal Water Slurries. [Masters Thesis]. University of Alberta; 2014. Available from: https://era.library.ualberta.ca/files/xs55mc577

Author name may be incomplete

Georgia Tech

17. Yang, Mengmeng. Seismic imaging with extended image volumes and source estimation.

Degree: PhD, Earth and Atmospheric Sciences, 2020, Georgia Tech

URL: http://hdl.handle.net/1853/62832

► Seismic imaging is an important tool for the exploration and production of oil & gas, carbon sequestration, and the mitigation of geohazards. Through the process…
(more)

Subjects/Keywords: Extended image volumes; Low rank; Sparsity; Source estimation; Multiples

Record Details Similar Records

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Yang, M. (2020). Seismic imaging with extended image volumes and source estimation. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/62832

Chicago Manual of Style (16^{th} Edition):

Yang, Mengmeng. “Seismic imaging with extended image volumes and source estimation.” 2020. Doctoral Dissertation, Georgia Tech. Accessed April 18, 2021. http://hdl.handle.net/1853/62832.

MLA Handbook (7^{th} Edition):

Yang, Mengmeng. “Seismic imaging with extended image volumes and source estimation.” 2020. Web. 18 Apr 2021.

Vancouver:

Yang M. Seismic imaging with extended image volumes and source estimation. [Internet] [Doctoral dissertation]. Georgia Tech; 2020. [cited 2021 Apr 18]. Available from: http://hdl.handle.net/1853/62832.

Council of Science Editors:

Yang M. Seismic imaging with extended image volumes and source estimation. [Doctoral Dissertation]. Georgia Tech; 2020. Available from: http://hdl.handle.net/1853/62832

University of California – Berkeley

18.
Ong, Frank.
* Low* Dimensional Methods for High Dimensional Magnetic Resonance Imaging.

Degree: Electrical Engineering & Computer Sciences, 2018, University of California – Berkeley

URL: http://www.escholarship.org/uc/item/27d0k54z

► Magnetic Resonance Imaging (MRI) is an amazing imaging modality in many aspects. It offers one of the best imaging contrast for visualizing soft issues. It…
(more)

Subjects/Keywords: Electrical engineering; Compressed sensing; Dynamic Imaging; Low rank; MRI

Record Details Similar Records

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Ong, F. (2018). Low Dimensional Methods for High Dimensional Magnetic Resonance Imaging. (Thesis). University of California – Berkeley. Retrieved from http://www.escholarship.org/uc/item/27d0k54z

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Ong, Frank. “Low Dimensional Methods for High Dimensional Magnetic Resonance Imaging.” 2018. Thesis, University of California – Berkeley. Accessed April 18, 2021. http://www.escholarship.org/uc/item/27d0k54z.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Ong, Frank. “Low Dimensional Methods for High Dimensional Magnetic Resonance Imaging.” 2018. Web. 18 Apr 2021.

Vancouver:

Ong F. Low Dimensional Methods for High Dimensional Magnetic Resonance Imaging. [Internet] [Thesis]. University of California – Berkeley; 2018. [cited 2021 Apr 18]. Available from: http://www.escholarship.org/uc/item/27d0k54z.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Ong F. Low Dimensional Methods for High Dimensional Magnetic Resonance Imaging. [Thesis]. University of California – Berkeley; 2018. Available from: http://www.escholarship.org/uc/item/27d0k54z

Not specified: Masters Thesis or Doctoral Dissertation

19. Torchio, Riccardo. Une extension de la méthode PEEC non structurée aux problèmes magnétiques, temporels et stochastiques : Extending the Unstructured PEEC Method to Magnetic, Transient, and Stochastic Electromagnetic Problems.

Degree: Docteur es, Génie électrique, 2019, Université Grenoble Alpes (ComUE); Università degli studi (Padoue, Italie)

URL: http://www.theses.fr/2019GREAT066

►

L'objectif principal de cette thèse est d'étendre et d'améliorer la précision de la méthode des circuits équivalents à éléments partiels non structurés (Unstructured PEEC). L'intérêt… (more)

Subjects/Keywords: Formulations; Intégrale; Électromagnétiques; Stochastique; Integral; Electromagnetic; Formulation; Peec; Low-Rank; 620

Record Details Similar Records

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Torchio, R. (2019). Une extension de la méthode PEEC non structurée aux problèmes magnétiques, temporels et stochastiques : Extending the Unstructured PEEC Method to Magnetic, Transient, and Stochastic Electromagnetic Problems. (Doctoral Dissertation). Université Grenoble Alpes (ComUE); Università degli studi (Padoue, Italie). Retrieved from http://www.theses.fr/2019GREAT066

Chicago Manual of Style (16^{th} Edition):

Torchio, Riccardo. “Une extension de la méthode PEEC non structurée aux problèmes magnétiques, temporels et stochastiques : Extending the Unstructured PEEC Method to Magnetic, Transient, and Stochastic Electromagnetic Problems.” 2019. Doctoral Dissertation, Université Grenoble Alpes (ComUE); Università degli studi (Padoue, Italie). Accessed April 18, 2021. http://www.theses.fr/2019GREAT066.

MLA Handbook (7^{th} Edition):

Torchio, Riccardo. “Une extension de la méthode PEEC non structurée aux problèmes magnétiques, temporels et stochastiques : Extending the Unstructured PEEC Method to Magnetic, Transient, and Stochastic Electromagnetic Problems.” 2019. Web. 18 Apr 2021.

Vancouver:

Torchio R. Une extension de la méthode PEEC non structurée aux problèmes magnétiques, temporels et stochastiques : Extending the Unstructured PEEC Method to Magnetic, Transient, and Stochastic Electromagnetic Problems. [Internet] [Doctoral dissertation]. Université Grenoble Alpes (ComUE); Università degli studi (Padoue, Italie); 2019. [cited 2021 Apr 18]. Available from: http://www.theses.fr/2019GREAT066.

Council of Science Editors:

Torchio R. Une extension de la méthode PEEC non structurée aux problèmes magnétiques, temporels et stochastiques : Extending the Unstructured PEEC Method to Magnetic, Transient, and Stochastic Electromagnetic Problems. [Doctoral Dissertation]. Université Grenoble Alpes (ComUE); Università degli studi (Padoue, Italie); 2019. Available from: http://www.theses.fr/2019GREAT066

Colorado School of Mines

20.
Yang, Dehui.
Structured *low*-*rank* matrix recovery via optimization methods.

Degree: PhD, Electrical Engineering, 2018, Colorado School of Mines

URL: http://hdl.handle.net/11124/172154

► From single-molecule microscopy in biology, to collaborative filtering in recommendation systems, to quantum state tomography in physics, many scientific discoveries involve solving ill-posed inverse problems,…
(more)

Subjects/Keywords: matrix completion; models; super-resolution; modal analysis; low-rank; optimization

Record Details Similar Records

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Yang, D. (2018). Structured low-rank matrix recovery via optimization methods. (Doctoral Dissertation). Colorado School of Mines. Retrieved from http://hdl.handle.net/11124/172154

Chicago Manual of Style (16^{th} Edition):

Yang, Dehui. “Structured low-rank matrix recovery via optimization methods.” 2018. Doctoral Dissertation, Colorado School of Mines. Accessed April 18, 2021. http://hdl.handle.net/11124/172154.

MLA Handbook (7^{th} Edition):

Yang, Dehui. “Structured low-rank matrix recovery via optimization methods.” 2018. Web. 18 Apr 2021.

Vancouver:

Yang D. Structured low-rank matrix recovery via optimization methods. [Internet] [Doctoral dissertation]. Colorado School of Mines; 2018. [cited 2021 Apr 18]. Available from: http://hdl.handle.net/11124/172154.

Council of Science Editors:

Yang D. Structured low-rank matrix recovery via optimization methods. [Doctoral Dissertation]. Colorado School of Mines; 2018. Available from: http://hdl.handle.net/11124/172154

University of Minnesota

21. Jiang, Bo. Polynomial optimization: structures, algorithms, and engineering applications.

Degree: PhD, Industrial and Systems Engineering, 2013, University of Minnesota

URL: http://purl.umn.edu/159747

► As a fundamental model in Operations Research, polynomial optimization has been receiving increasingly more attention in the recent years, due to its versatile modern applications…
(more)

Subjects/Keywords: Approximation algorithms; Low-rank; Polynomial optimization; Tensor optimization

Record Details Similar Records

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Jiang, B. (2013). Polynomial optimization: structures, algorithms, and engineering applications. (Doctoral Dissertation). University of Minnesota. Retrieved from http://purl.umn.edu/159747

Chicago Manual of Style (16^{th} Edition):

Jiang, Bo. “Polynomial optimization: structures, algorithms, and engineering applications.” 2013. Doctoral Dissertation, University of Minnesota. Accessed April 18, 2021. http://purl.umn.edu/159747.

MLA Handbook (7^{th} Edition):

Jiang, Bo. “Polynomial optimization: structures, algorithms, and engineering applications.” 2013. Web. 18 Apr 2021.

Vancouver:

Jiang B. Polynomial optimization: structures, algorithms, and engineering applications. [Internet] [Doctoral dissertation]. University of Minnesota; 2013. [cited 2021 Apr 18]. Available from: http://purl.umn.edu/159747.

Council of Science Editors:

Jiang B. Polynomial optimization: structures, algorithms, and engineering applications. [Doctoral Dissertation]. University of Minnesota; 2013. Available from: http://purl.umn.edu/159747

University of Minnesota

22. Guhaniyogi, Rajarshi. On Bayesian hierarchical modelling for large spatial datasets.

Degree: PhD, Biostatistics, 2012, University of Minnesota

URL: http://purl.umn.edu/122854

► We propose a class of fully process-based *low*-*rank* spatially-varying cross-covariance matrices that produce non-degenerate spatial processes and that effectively capture non-stationary covariances among the multiple…
(more)

Subjects/Keywords: Hierarchical Bayesiam Model; Low rank Models; Predictive processes; Spatial statistics; Biostatistics

Record Details Similar Records

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Guhaniyogi, R. (2012). On Bayesian hierarchical modelling for large spatial datasets. (Doctoral Dissertation). University of Minnesota. Retrieved from http://purl.umn.edu/122854

Chicago Manual of Style (16^{th} Edition):

Guhaniyogi, Rajarshi. “On Bayesian hierarchical modelling for large spatial datasets.” 2012. Doctoral Dissertation, University of Minnesota. Accessed April 18, 2021. http://purl.umn.edu/122854.

MLA Handbook (7^{th} Edition):

Guhaniyogi, Rajarshi. “On Bayesian hierarchical modelling for large spatial datasets.” 2012. Web. 18 Apr 2021.

Vancouver:

Guhaniyogi R. On Bayesian hierarchical modelling for large spatial datasets. [Internet] [Doctoral dissertation]. University of Minnesota; 2012. [cited 2021 Apr 18]. Available from: http://purl.umn.edu/122854.

Council of Science Editors:

Guhaniyogi R. On Bayesian hierarchical modelling for large spatial datasets. [Doctoral Dissertation]. University of Minnesota; 2012. Available from: http://purl.umn.edu/122854

University of Minnesota

23.
Mardani, Morteza.
Leveraging Sparsity and *Low* *Rank* for Large-Scale Networks and Data Science.

Degree: PhD, Electrical/Computer Engineering, 2015, University of Minnesota

URL: http://hdl.handle.net/11299/174873

► We live in an era of ``data deluge," with pervasive sensors collecting massive amounts of information on every bit of our lives, churning out enormous…
(more)

Subjects/Keywords: Big data; Large-scale networks; learning; Low rank; Sparsity

Record Details Similar Records

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Mardani, M. (2015). Leveraging Sparsity and Low Rank for Large-Scale Networks and Data Science. (Doctoral Dissertation). University of Minnesota. Retrieved from http://hdl.handle.net/11299/174873

Chicago Manual of Style (16^{th} Edition):

Mardani, Morteza. “Leveraging Sparsity and Low Rank for Large-Scale Networks and Data Science.” 2015. Doctoral Dissertation, University of Minnesota. Accessed April 18, 2021. http://hdl.handle.net/11299/174873.

MLA Handbook (7^{th} Edition):

Mardani, Morteza. “Leveraging Sparsity and Low Rank for Large-Scale Networks and Data Science.” 2015. Web. 18 Apr 2021.

Vancouver:

Mardani M. Leveraging Sparsity and Low Rank for Large-Scale Networks and Data Science. [Internet] [Doctoral dissertation]. University of Minnesota; 2015. [cited 2021 Apr 18]. Available from: http://hdl.handle.net/11299/174873.

Council of Science Editors:

Mardani M. Leveraging Sparsity and Low Rank for Large-Scale Networks and Data Science. [Doctoral Dissertation]. University of Minnesota; 2015. Available from: http://hdl.handle.net/11299/174873

Georgia Tech

24.
Rangel Walteros, Pedro Andres.
A non-asymptotic study of *low*-*rank* estimation of smooth kernels on graphs.

Degree: PhD, Mathematics, 2014, Georgia Tech

URL: http://hdl.handle.net/1853/52988

► This dissertation investigates the problem of estimating a kernel over a large graph based on a sample of noisy observations of linear measurements of the…
(more)

Subjects/Keywords: Low-rank matrix completion; Kernels on graphs; High dimensional probability

Record Details Similar Records

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Rangel Walteros, P. A. (2014). A non-asymptotic study of low-rank estimation of smooth kernels on graphs. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/52988

Chicago Manual of Style (16^{th} Edition):

Rangel Walteros, Pedro Andres. “A non-asymptotic study of low-rank estimation of smooth kernels on graphs.” 2014. Doctoral Dissertation, Georgia Tech. Accessed April 18, 2021. http://hdl.handle.net/1853/52988.

MLA Handbook (7^{th} Edition):

Rangel Walteros, Pedro Andres. “A non-asymptotic study of low-rank estimation of smooth kernels on graphs.” 2014. Web. 18 Apr 2021.

Vancouver:

Rangel Walteros PA. A non-asymptotic study of low-rank estimation of smooth kernels on graphs. [Internet] [Doctoral dissertation]. Georgia Tech; 2014. [cited 2021 Apr 18]. Available from: http://hdl.handle.net/1853/52988.

Council of Science Editors:

Rangel Walteros PA. A non-asymptotic study of low-rank estimation of smooth kernels on graphs. [Doctoral Dissertation]. Georgia Tech; 2014. Available from: http://hdl.handle.net/1853/52988

Georgia Tech

25.
Kannan, Ramakrishnan.
Scalable and distributed constrained *low* *rank* * approximations*.

Degree: PhD, Computational Science and Engineering, 2016, Georgia Tech

URL: http://hdl.handle.net/1853/54962

► *Low* *rank* approximation is the problem of finding two *low* *rank* factors W and H such that the *rank*(WH) << *rank*(A) and A ≈ WH.…
(more)

Subjects/Keywords: Distributed; Scalable; NMF; Communication avoiding; HPC; Low rank approximation

Record Details Similar Records

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Kannan, R. (2016). Scalable and distributed constrained low rank approximations. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/54962

Chicago Manual of Style (16^{th} Edition):

Kannan, Ramakrishnan. “Scalable and distributed constrained low rank approximations.” 2016. Doctoral Dissertation, Georgia Tech. Accessed April 18, 2021. http://hdl.handle.net/1853/54962.

MLA Handbook (7^{th} Edition):

Kannan, Ramakrishnan. “Scalable and distributed constrained low rank approximations.” 2016. Web. 18 Apr 2021.

Vancouver:

Kannan R. Scalable and distributed constrained low rank approximations. [Internet] [Doctoral dissertation]. Georgia Tech; 2016. [cited 2021 Apr 18]. Available from: http://hdl.handle.net/1853/54962.

Council of Science Editors:

Kannan R. Scalable and distributed constrained low rank approximations. [Doctoral Dissertation]. Georgia Tech; 2016. Available from: http://hdl.handle.net/1853/54962

Georgia Tech

26. Xia, Dong. Statistical inference for large matrices.

Degree: PhD, Mathematics, 2016, Georgia Tech

URL: http://hdl.handle.net/1853/55632

► This thesis covers two topics on matrix analysis and estimation in machine learning and statistics. The first topic is about density matrix estimation with application…
(more)

Subjects/Keywords: Low rank; Matrix estimation; Singular vectors; Random perturbation

Record Details Similar Records

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Xia, D. (2016). Statistical inference for large matrices. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/55632

Chicago Manual of Style (16^{th} Edition):

Xia, Dong. “Statistical inference for large matrices.” 2016. Doctoral Dissertation, Georgia Tech. Accessed April 18, 2021. http://hdl.handle.net/1853/55632.

MLA Handbook (7^{th} Edition):

Xia, Dong. “Statistical inference for large matrices.” 2016. Web. 18 Apr 2021.

Vancouver:

Xia D. Statistical inference for large matrices. [Internet] [Doctoral dissertation]. Georgia Tech; 2016. [cited 2021 Apr 18]. Available from: http://hdl.handle.net/1853/55632.

Council of Science Editors:

Xia D. Statistical inference for large matrices. [Doctoral Dissertation]. Georgia Tech; 2016. Available from: http://hdl.handle.net/1853/55632

University of Texas – Austin

27.
-0961-6947.
Seismic modeling and imaging in complex media using *low*-*rank* approximation.

Degree: PhD, Geological Sciences, 2016, University of Texas – Austin

URL: http://hdl.handle.net/2152/45954

► Seismic imaging in geologically complex areas, such as sub-salt or attenuating areas, has been one of the greatest challenges in hydrocarbon exploration. Increasing the fidelity…
(more)

Subjects/Keywords: Seismic modeling; Reverse-time migration; Low-rank approximation; Seismic attenuation

Record Details Similar Records

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

-0961-6947. (2016). Seismic modeling and imaging in complex media using low-rank approximation. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/45954

Author name may be incomplete

Chicago Manual of Style (16^{th} Edition):

-0961-6947. “Seismic modeling and imaging in complex media using low-rank approximation.” 2016. Doctoral Dissertation, University of Texas – Austin. Accessed April 18, 2021. http://hdl.handle.net/2152/45954.

Author name may be incomplete

MLA Handbook (7^{th} Edition):

-0961-6947. “Seismic modeling and imaging in complex media using low-rank approximation.” 2016. Web. 18 Apr 2021.

Author name may be incomplete

Vancouver:

-0961-6947. Seismic modeling and imaging in complex media using low-rank approximation. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2016. [cited 2021 Apr 18]. Available from: http://hdl.handle.net/2152/45954.

Author name may be incomplete

Council of Science Editors:

-0961-6947. Seismic modeling and imaging in complex media using low-rank approximation. [Doctoral Dissertation]. University of Texas – Austin; 2016. Available from: http://hdl.handle.net/2152/45954

Author name may be incomplete

Purdue University

28.
Hou, Yangyang.
*Low**rank* methods for optimizing clustering.

Degree: PhD, Computer Science, 2016, Purdue University

URL: https://docs.lib.purdue.edu/open_access_dissertations/935

► Complex optimization models and problems in machine learning often have the majority of information in a *low* *rank* subspace. By careful exploitation of these…
(more)

Subjects/Keywords: Applied sciences; Clustering; Low rank methods; Computer Sciences

Record Details Similar Records

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Hou, Y. (2016). Low rank methods for optimizing clustering. (Doctoral Dissertation). Purdue University. Retrieved from https://docs.lib.purdue.edu/open_access_dissertations/935

Chicago Manual of Style (16^{th} Edition):

Hou, Yangyang. “Low rank methods for optimizing clustering.” 2016. Doctoral Dissertation, Purdue University. Accessed April 18, 2021. https://docs.lib.purdue.edu/open_access_dissertations/935.

MLA Handbook (7^{th} Edition):

Hou, Yangyang. “Low rank methods for optimizing clustering.” 2016. Web. 18 Apr 2021.

Vancouver:

Hou Y. Low rank methods for optimizing clustering. [Internet] [Doctoral dissertation]. Purdue University; 2016. [cited 2021 Apr 18]. Available from: https://docs.lib.purdue.edu/open_access_dissertations/935.

Council of Science Editors:

Hou Y. Low rank methods for optimizing clustering. [Doctoral Dissertation]. Purdue University; 2016. Available from: https://docs.lib.purdue.edu/open_access_dissertations/935

Georgia Tech

29.
Sharan, Shashin.
LARGE SCALE HIGH-FREQUENCY SEISMIC WAVEFIELD RECONSTRUCTION, ACQUISITION VIA *RANK* MINIMIZATION AND SPARSITY-PROMOTING SOURCE ESTIMATION.

Degree: PhD, Earth and Atmospheric Sciences, 2020, Georgia Tech

URL: http://hdl.handle.net/1853/64162

► Seismic data reconstruction on a dense periodic grid from seismic data acquired on a coarse grid is a common approach followed by most of the…
(more)

Subjects/Keywords: Sparsity-promoting; Low-Rank; Wavefield Reconstruction; Compressed Sensing

Record Details Similar Records

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Sharan, S. (2020). LARGE SCALE HIGH-FREQUENCY SEISMIC WAVEFIELD RECONSTRUCTION, ACQUISITION VIA RANK MINIMIZATION AND SPARSITY-PROMOTING SOURCE ESTIMATION. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/64162

Chicago Manual of Style (16^{th} Edition):

Sharan, Shashin. “LARGE SCALE HIGH-FREQUENCY SEISMIC WAVEFIELD RECONSTRUCTION, ACQUISITION VIA RANK MINIMIZATION AND SPARSITY-PROMOTING SOURCE ESTIMATION.” 2020. Doctoral Dissertation, Georgia Tech. Accessed April 18, 2021. http://hdl.handle.net/1853/64162.

MLA Handbook (7^{th} Edition):

Sharan, Shashin. “LARGE SCALE HIGH-FREQUENCY SEISMIC WAVEFIELD RECONSTRUCTION, ACQUISITION VIA RANK MINIMIZATION AND SPARSITY-PROMOTING SOURCE ESTIMATION.” 2020. Web. 18 Apr 2021.

Vancouver:

Sharan S. LARGE SCALE HIGH-FREQUENCY SEISMIC WAVEFIELD RECONSTRUCTION, ACQUISITION VIA RANK MINIMIZATION AND SPARSITY-PROMOTING SOURCE ESTIMATION. [Internet] [Doctoral dissertation]. Georgia Tech; 2020. [cited 2021 Apr 18]. Available from: http://hdl.handle.net/1853/64162.

Council of Science Editors:

Sharan S. LARGE SCALE HIGH-FREQUENCY SEISMIC WAVEFIELD RECONSTRUCTION, ACQUISITION VIA RANK MINIMIZATION AND SPARSITY-PROMOTING SOURCE ESTIMATION. [Doctoral Dissertation]. Georgia Tech; 2020. Available from: http://hdl.handle.net/1853/64162

University of Cambridge

30. Maji, Partha. Model-Architecture Co-design of Deep Neural Networks for Embedded Systems.

Degree: PhD, 2020, University of Cambridge

URL: https://www.repository.cam.ac.uk/handle/1810/307488

► In deep learning, a convolutional neural network (ConvNet or CNN) is a powerful tool for building interesting embedded applications that use data to make predictions.…
(more)

Subjects/Keywords: Neural Network; Convolutional Neural Network; Optimisation; SIMD; Low-rank; Compression; Accelerators

Record Details Similar Records

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Maji, P. (2020). Model-Architecture Co-design of Deep Neural Networks for Embedded Systems. (Doctoral Dissertation). University of Cambridge. Retrieved from https://www.repository.cam.ac.uk/handle/1810/307488

Chicago Manual of Style (16^{th} Edition):

Maji, Partha. “Model-Architecture Co-design of Deep Neural Networks for Embedded Systems.” 2020. Doctoral Dissertation, University of Cambridge. Accessed April 18, 2021. https://www.repository.cam.ac.uk/handle/1810/307488.

MLA Handbook (7^{th} Edition):

Maji, Partha. “Model-Architecture Co-design of Deep Neural Networks for Embedded Systems.” 2020. Web. 18 Apr 2021.

Vancouver:

Maji P. Model-Architecture Co-design of Deep Neural Networks for Embedded Systems. [Internet] [Doctoral dissertation]. University of Cambridge; 2020. [cited 2021 Apr 18]. Available from: https://www.repository.cam.ac.uk/handle/1810/307488.

Council of Science Editors:

Maji P. Model-Architecture Co-design of Deep Neural Networks for Embedded Systems. [Doctoral Dissertation]. University of Cambridge; 2020. Available from: https://www.repository.cam.ac.uk/handle/1810/307488